SpeedWise® ML allows everyone to conduct cutting-edge machine learning practices and predictive analysis – no coding necessary

HOUSTON, TEXAS (April 28, 2021) – QRI officially announces the availability of SpeedWise^® ML (SML) in AWS Marketplace. Amazon Web Services (AWS) customers can purchase and conduct cutting-edge machine learning practices and predictive analysis with a few simple steps. No prior experience or coding knowledge needed.

The goal of machine learning is to unveil trends, patterns, and correlations from multidimensional data. However, it remains a challenging task to develop and deploy machine learning models effectively within organizations. In fact, it is not uncommon to spend several months and even years to implement machine learning and deploy it in the production environment, even for an integrated team involving domain experts, data engineers, data/machine learning scientists, computer scientists, and IT.

“SML brings practicality to complex problem solving,” said Dr. Nansen G. Saleri, CEO & Chairman of QRI. “If you have lots of data and can estimate what factors or variables possibly affect outcomes, SML distills big data into clear solutions. I call this ‘practical AI’.”

SpeedWise^® ML is a web-based software platform that allows any company or organization that has data that is not yet fully exploited to conduct cutting-edge machine learning practices and predictive analysis. Through the platform, everyone, with or without a deep understanding of machine learning, will be able to deliver high-quality production-level models through a few mouse clicks within minutes, without typing a single line of code.

About SpeedWise^® ML
Powered by Quantum Reservoir Impact’s (QRI) enterprise-grade automatic machine learning algorithm, SpeedWise^® ML aims to generate machine learning models (both unsupervised and supervised learning) that can unveil hidden trends and patterns from data, offering new solutions or opportunities within your organization that were not visible before. The platform guides the user through framing the problem, and through the entire data-to-model process, and is also flexible for the data engineer, data scientist or machine learning expert to access settings every step of the way.

Media: Contact QRI Media Relations